Unsupervised Learning with Jacob Effron
Jun 12, 2026 · 1 hr 7 min · 11 segments
Six months after their last roundup, Jacob sits down with Ari Morcos (Datology AI CEO, former Meta AI researcher) and Rob Toews (Radical Ventures partner, Forbes AI columnist) to take stock of an AI…
Ari MorcosGuest
Rob ToewsGuestJacob EffronHostUh, what's like the single biggest thing that has changed in how you're thinking about the landscape since we last talked? And maybe, uh, Ari, I'll start with you.
Yeah, I mean, I think the, the most obvious thing that has changed, uh, over the last six months is starting to see the coding agents really start to work at longer time horizons, um, right.
I think that was just starting when we recorded our, our last episode at the end of, uh, '25.
Feel like everyone went away over Christmas break and was like, "Holy crap, like, these things really work." [laughs]
Yeah, and they, and I think it, it starts to show how there are these thresholds where if you go beyond a threshold, it can become a lot more valuable.
Um, and obviously, that's driven the, the massive rise in, in token spending and the whole token maxing idea and, and all this stuff.
Um, but I think we're really starting to now see the shift of engineers at least kind of almost all moving from ICs to managers of agents.
Um, that's been something that's been very noticeable within Datology, for example, um, over the last, uh, number of months, is seeing more and more people starting to now context switch between managing different agents, uh, rather than just kind of, you know, working on the one thing, and that is enabled by having these, um, you know, agents be able to run long enough and actually be useful in various ways.
I mean, everyone likes to ask, uh, top AI researchers like yourself, like how much more productive has it made you in your, uh, in your work?
It makes you a lot more productive in some ways, right? But it also, um, produces a lot of challenges as well.
Like, one of the things that we're struggling with is now, um, it's a lot easier to produce a massive amount of code that can do something, but now you have this pretty massive understanding gap, and it's a lot easier to put slop, uh, into your code base.
I think a lot of times, though, the kinda top-line numbers tend to be overestimated, um, because it doesn't take into account some of these like later costs of like we now have big bottlenecks on reviews.
Um, you know, whatever, uh, it, it, it's hard to improve on an entire process, uh, because of, because of that.
Uh, what's like the single biggest thing that has changed in how you're thinking about the landscape since we last talked? And maybe, uh, Ari, I'll start with you.
Yeah, I mean, I think the, the most obvious thing that has changed, uh, over the last six months is starting to see the coding agents really start to work at longer time horizons, um, right.
I think that was just starting when we recorded our, our last episode at the end of, uh, '25.
Feel like everyone went away over Christmas break and was like, "Holy crap, like, these things really work." [laughs]
Yeah, and they, and I think it, it starts to show how there are these thresholds where if you go beyond a threshold, it can become a lot more valuable.
Um, and obviously, that's driven the, the massive rise in, in token spending and the whole token maxing idea and, and all this stuff.
Um, but I think we're really starting to now see the shift of engineers at least kind of almost all moving from ICs to managers of agents.
Um, that's been something that's been very noticeable within Datology, for example, um, over the last, uh, number of months, is seeing more and more people starting to now context switch between managing different agents, uh, rather than just kind of, you know, working on the one thing, and that is enabled by having these, um, you know, agents be able to run long enough and actually be useful in various ways.
I mean, everyone likes to ask, uh, top AI researchers like yourself, like how much more productive has it made you in your, uh, in your work?
It makes you a lot more productive in some ways, right? But it also, um, produces a lot of challenges as well.
Like, one of the things that we're struggling with is now, um, it's a lot easier to produce a massive amount of code that can do something, but now you have this pretty massive understanding gap, and it's a lot easier to put slop, uh, into your code base.
I think a lot of times, though, the kinda top-line numbers tend to be overestimated, um, because it doesn't take into account some of these like later costs of like we now have big bottlenecks on reviews.
Um, you know, whatever, uh, it, it, it's hard to improve on an entire process, uh, because of, because of that.
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